Join one of the world's most innovative technology organizations and help shape the infrastructure that powers it. At JPMorganChase, our engineers don't just build software — they build the platforms that thousands of engineers rely on every day. If you're passionate about cloud-native engineering, security, and developer experience, this is your opportunity to make a lasting impact at scale.
As a Lead Software Engineer at JPMorganChase within the Container Platforms group, you will be a core contributor to the Security & Identity team, designing and evolving Kubernetes-based platform capabilities that enable engineering teams to deploy, run, and scale services safely and efficiently. You will work closely with peers across public and private platforms, partnering on security, identity, and usability to deliver a best-in-class developer experience. This is a hands-on, end-to-end engineering role where your contributions will directly improve platform reliability, operational maturity, and engineering culture across the firm.
Job responsibilities
- Design, build, and maintain Kubernetes platform capabilities including cluster services, controllers and operators, admission policies, platform APIs, and developer tooling
- Develop backend services and automation in Go and Python to improve platform reliability, usability, and self-service for engineering consumers
- Create and maintain delivery workflows that standardize engineering practices and reduce operational toil across the platform
- Improve observability and operational readiness through monitoring, logging, tracing, alerting, runbook development, and on-call practices
- Partner with security and risk stakeholders to implement secure-by-default patterns across identity, policy, network controls, and secrets management
- Contribute to technical direction by authoring design documents, participating in architecture reviews, and helping define engineering standards and best practices
- Mentor and support fellow engineers through code reviews, pairing and mob programming sessions, and constructive, pragmatic feedback
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Strong practical experience with Kubernetes, including workload scheduling, services and networking, storage, role-based access control, upgrades, cluster operations, and troubleshooting
- Solid cloud development experience, including designing distributed systems, deploying and operating services in cloud environments, and understanding of reliability and scaling principles
- Proficiency in Go and Python, with the ability to apply these languages to build platform services and automation tooling
- Demonstrated ability to collaborate effectively in a team setting, including experience with or openness to pair programming and mob programming practices
- Strong engineering fundamentals including data structures, networking basics, Linux and container fundamentals, and secure coding practices
- Ability to take ambiguous requirements, propose solutions, and deliver iteratively with clear and consistent communication
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
- Experience building platform components such as operators and controllers, admission webhooks, service meshes, ingress and gateway patterns, or multi-cluster tooling
- Familiarity with infrastructure-as-code and automation practices, including tools such as Terraform, Helm, Kustomize, Argo CD, Flux, or continuous integration systems
- Experience with observability stacks and site reliability engineering practices, including service level indicators and objectives, incident response, and post-incident reviews
- Exposure to regulated environments and implementing security and compliance controls without compromising developer experience